Chapter 2 Electricity profiles

This chapter provides electricity profiles and a R coding technique. Electricity profiles are illustrated to give an insight into profile behavior. R coding technique are given in order to understand the process flow and manage the big data obtained from several sources in the organization.

2.1 Initial settings

Before the project starts, packages are required as follows:

• A tidyverse package is a bunch of packages to handle with a data analysis (for more information click here). It consists of data manipulation and visualization as follows:
  • A ggplot2 package is for data visualization.
  • A dplyr package is for data manipulation.
  • A tidyr package is for data tidying.
  • A readr package is for data import.
  • A purrr package is for functional programming.
  • A tibble package is a modern reimagining of the data.frame.
  • A stringr package is for strings.
  • A forcats package is for factors.
  • A lubridate package is for date and time.
• A readxl package is used for read data from Excel into R.
• A scales package is applied to scale plots in the ggplot2 package.
• A glue package interprets strings literal. The package embeds R expressions and inserts into argument string.
• A knitr package is a lightweight API’s designed to give users full control of the output without heavy coding work. In this project, the package is used for making a HTML.

library(tidyverse) #For data manipulation
library(readxl) #For reading the excel sheet
library(scales)
library(glue)
library(knitr)
library(plotly)

Theme and lines for figures are set in a variable named Themeline as follows:

• theme_bw() function provides a black and white theme.
• theme() function applied for setting theme and line represented in plots.
• linepalette1 and linepalette1 variables set line colors.

ThemeLine <- 
  theme_bw() +
  theme(
    panel.border=element_rect(fill=NA),
    panel.grid.minor = element_line(color = NA), 
    #    axis.title=element_text(size=5),
    #    axis.text.x = element_text(hjust=1,size = 10, angle = 0),
    axis.line=element_line(colour="black"),
    panel.background=element_rect(fill = "white"),
    panel.grid.major.x=element_line(linetype="dashed",colour="grey",linewidth = 0.5),
    panel.grid.major.y = element_blank(),
    # panel.grid.major=element_blank(),
    strip.background=element_rect(fill="white", colour="white"),
    strip.text.x = element_text(size=10, colour = "black", angle = 0,face="bold"),
    axis.text.x=element_text(size = 10,angle=45, vjust=0.9, hjust=1, margin = unit(c(t = 0.3, r = 0, b = 0, l = 0), "cm")),
    axis.text.y=element_text(size = 10,margin = unit(c(t = 0, r = 0.3, b = 0, l = 0), "cm")),
    legend.text = element_text(size = 10),
    legend.title = element_text(size = 10),
    axis.ticks.length=unit(-0.15,"cm")
  )
linepalette1 <- c("#4DAF4A","#FF7F00","#377EB8","#E41A1C","#984EA3","#F781BF","#8DD3C7","#FB8072","#80B1D3","#FDB462","#B3DE69","#FCCDE5","#D9D9D9","#BC80BD","#CCEBC5","#FFED6F","#7f878f","#A65628","#FFFF33")
linepalette2 <- c("#E41A1C","#FF7F00","#377EB8","#B3DE69","#4DAF4A","#984EA3","#F781BF","#8DD3C7","#FB8072","#80B1D3","#FDB462","#FCCDE5","#D9D9D9","#BC80BD","#CCEBC5","#FFED6F","#7f878f","#A65628","#FFFF33")

Lists are create to store variables.

#----- Make a list of profile to store data -----####

profiledata <- list()
profilefigure <- list()
summarydata <- list()

#----- Make variable for output directory -----####
outfigdir <- c("figures/")

2.2 The EGAT electrity sale profiles

2.2.1 The 2019 MEA EGAT sale profile

The electricity sale profile from EGAT is given by the Power System Control and Operation Division (ฝ่ายควบคุมระบบกำลังไฟฟ้า).

#----- The 2019 MEA EGAT sale profile ----####
# Profile data ####
profile <- 
  read_excel("raw_data/raw_data_profiles/02_Hourly Sale_NetGen_2019.xlsx",
           sheet = "Load Curve",
           range = "C3:D17523"
            ) %>% 
  select(datetime = `Date/Time`, MEA) %>% 
  mutate(date = date(datetime),
         time = format(as.POSIXct(datetime),"%H:%M:%S"),
         year = year(datetime),
         month = month(datetime),
         day = day(datetime)) %>% 
  select(datetime, date, time, year, month, day, MEA)

Table 2.1: The 2019 EGAT sale to MEA profile

datetime	date	time	year	month	day	MEA
2019-01-01 00:00:00	2019-01-01	00:00:00	2019	1	1	3361.083
2019-01-01 00:30:00	2019-01-01	00:30:00	2019	1	1	3244.405
2019-01-01 01:00:00	2019-01-01	01:00:00	2019	1	1	3199.672
2019-01-01 01:30:00	2019-01-01	01:30:00	2019	1	1	3153.101
2019-01-01 02:00:00	2019-01-01	02:00:00	2019	1	1	3085.642

The peak, minimun in the unit of MW, and an electricity sale to MEA in GWh from the profile are calculated. The electricity sale to MEA (in GWh) is calculated by the summation of profile in MW divided by 2000.

The reason to divide by 2,000 is that the profile is given a time stamp in every 30-min. There are 17,520 values. Therefore, the profile is divided by 2 to get the results in every 1 hr.. We divided the output by 1,000 to convert the unit from MWh to GWh (see equation (2.1)).

\[\begin{equation} Ene_{t}=\frac{\sum_{i=1}^{17520} (profile_{t,i})}{2*1000} \tag{2.1} \end{equation}\]

Where, \(Ene_{t}\) denotes an calculated energy (electricity) in year \(t\).
\(profile_{t,i}\) denotes a generation in the unit of MW in year \(t\) at a time stamp \(i\).
\(i\) denotes a time stamp in a 30-minute interval. Therefore, there are 17,520 intervals in a year (8,760 hrs.).

# Summary data ####
maxv <- ceiling(max(profile$MEA)) # Get a peak MW
minv <- floor(min(profile$MEA)) # Get a min MW
energy <- sum(profile$MEA)/2000 # Calculate the energy

The peak and minimum MW days are also needed to assess in order to provide the maximum and minimum a saled electricity from EGAT to MEA.

peak_day <- profile %>% #Find a peak day
            group_by(year) %>% 
            filter(MEA == max(MEA)) %>% 
            pull(datetime)
min_day <- profile %>% #Find a peak day
           group_by(year) %>% 
           filter(MEA == min(MEA)) %>% 
           pull(datetime)

Load factor is calculated by the energy in equation (2.1) divided a maximum energy. A maximum energy is the peak multiplied by 8760 hours. Result is presented in a percentage.

\[\begin{equation} LF_{t}=\frac{Energy_{t}}{Peak_{d,h} * 8760}*100 \tag{2.2} \end{equation}\]

Where, \(LF_{t}\) denotes the load factor at year \(t\) in percentage.
\(Energy_{t}\) denotes the calculated energy from equation (2.1) at year \(t\) in GWh.
\(Peak_{d,h}\) denotes the peak electricity sale on the date \(d\) at time \(h\).

load_factor <- percent((energy*10^3)/(maxv*8760), 
                       accuracy = 0.01, 
                       decimal.mark = ".")

Create a table to store relevant data.

summary <- tibble(peak_day = peak_day,
                  min_day = min_day,
                  peak_mw = maxv, 
                  min_mw = minv, 
                  energy_gwh = energy,
                  load_factor = load_factor) # combine all data in 1 table

In 2019, the electricity sale to MEA by EGAT reached it peak 9,526 MW on 2019-04-25 14:00:00. The energy (electricity) sale to MEA was 54,939.73 GWh. The summary of the electricity sale to MEA by EGAT in 2019 is given in Table 2.2.

Table 2.2: Summary of EGAT sale to MEA in 2019

peak_day	min_day	peak_mw	min_mw	energy_gwh	load_factor
2019-04-25 14:00:00	2019-01-01 05:00:00	9526	2748	54939.73	65.84%

The 2019 electricity sale profile from EGAT to MEA is illustrated (see Figure 2.1).

profile_plot <-
    ggplot() + 
    geom_line(data=profile, 
              aes(x = datetime, 
                  y = MEA,
                  group = month,
                  color = as.factor(month)),
              show.legend = FALSE) +
    ThemeLine +
    labs(x = NULL,
         y = "EGAT sale to MEA (MW)")+
    scale_x_datetime(breaks=date_breaks("1 month"), 
                     labels=date_format("%b %y")) +
    scale_y_continuous(breaks = seq(0, round(maxv,-3),1000),
                       limits = c(0, round(maxv, -3))) +  
    scale_color_manual(values = linepalette1) +
    geom_point(data=summary,
               aes(x = peak_day, y = peak_mw))+
    geom_text(data = summary,
              aes(x = peak_day, y = round(maxv, -3)),
              label = glue("Peak {maxv} MW \n@ {peak_day}"))+
    geom_point(data=summary,
               aes(x = min_day, y = min_mw))+
    geom_text(data = summary,
              aes(x = min_day, y = round(minv, -3)),
              label = glue("Minimum {minv} MW \n@ {min_day}"),
              hjust = 0,
              vjust = 1.5)

Figure 2.1: EGAT electricity sale profile to MEA in 2019.

Outputs are saved. Outputs are listed as follows:

• Create a file name mea_egtsle_2019.png
• Save a plot in a directory using ggsave() function
• Save profile data in a profiledata list
• Save figures in a profilefigure list
• Save summary data in a summarydata list

outputfigure <- paste0(outfigdir, "mea_egtsle_2019.png")
ggsave(profile_plot, file = outputfigure, dpi = 150, width = 15, height = 5, units = "in", limitsize = FALSE)
profiledata <- c(profiledata, list("mea_egtsle_2019" = profile))
profilefigure <- c(profilefigure, list("mea_egtsle_2019" = profile_plot))
summarydata <- c(summarydata, list("sum_mea_egtsle_2019" = summary))

2.2.2 The 2019 PEA-R1 (Central region) EGAT sale profile

Read a profile data

profile <- 
  read_excel("raw_data/raw_data_profiles/02_Hourly Sale_NetGen_2019.xlsx",
             sheet = "Load Curve",
             range = "C3:E17523"
  ) %>% 
  select(datetime = `Date/Time`, PEA_R1) %>% 
  mutate(date = date(datetime),
         time = format(as.POSIXct(datetime),"%H:%M:%S"),
         year = year(datetime),
         month = month(datetime),
         day = day(datetime)) %>% 
  select(datetime, date, time, year, month, day, PEA_R1)

Table 2.3: The 2019 EGAT sale to PEA R1 profile

datetime	date	time	year	month	day	PEA_R1
2019-01-01 00:00:00	2019-01-01	00:00:00	2019	1	1	5036.982
2019-01-01 00:30:00	2019-01-01	00:30:00	2019	1	1	4907.355
2019-01-01 01:00:00	2019-01-01	01:00:00	2019	1	1	4929.126
2019-01-01 01:30:00	2019-01-01	01:30:00	2019	1	1	4793.341
2019-01-01 02:00:00	2019-01-01	02:00:00	2019	1	1	4789.811

maxv <- ceiling(max(profile$PEA_R1)) # Get a peak MW
minv <- floor(min(profile$PEA_R1)) # Get a min MW
energy <- sum(profile$PEA_R1)/2000 # Calculate the energy
peak_day <- profile %>% #Find a peak day
  group_by(year) %>% 
  filter(PEA_R1 == max(PEA_R1)) %>% 
  pull(datetime)
min_day <- profile %>% #Find a peak day
  group_by(year) %>% 
  filter(PEA_R1 == min(PEA_R1)) %>% 
  pull(datetime)
load_factor <- percent((energy*10^3)/(maxv*8760), 
                       accuracy = 0.01, 
                       decimal.mark = ".")
summary <- tibble(peak_day = peak_day,
                  min_day = min_day,
                  peak_mw = maxv, 
                  min_mw = minv, 
                  energy_gwh = energy,
                  load_factor = load_factor) 

Table 2.4: Summary of EGAT sale to PEA R1 in 2019

peak_day	min_day	peak_mw	min_mw	energy_gwh	load_factor
2019-05-03 23:00:00	2019-01-01 12:00:00	11936	3293	80961.36	77.43%

The 2019 electricity sale profile from EGAT to PEA-R1 is illustrated (see Figure 2.2).

profile_plot <-
  ggplot() + 
  geom_line(data=profile, 
            aes(x = datetime, 
                y = PEA_R1,
                group = month,
                color = as.factor(month)),
            show.legend = FALSE) +
  ThemeLine +
  labs(x = NULL,
       y = "EGAT sale to PEA R1 (Central region) (MW)")+
  scale_x_datetime(breaks=date_breaks("1 month"), 
                   labels=date_format("%b %y")) +
  scale_y_continuous(breaks = seq(0, round(maxv,-3),1000),
                     limits = c(0, round(maxv, -3))) +  
  scale_color_manual(values = linepalette1) +
  geom_point(data=summary,
             aes(x = peak_day, y = peak_mw))+
  geom_text(data = summary,
            aes(x = peak_day, y = round(maxv, -3)),
            label = glue("Peak {maxv} MW \n@ {peak_day}"))+
  geom_point(data=summary,
             aes(x = min_day, y = min_mw))+
  geom_text(data = summary,
            aes(x = min_day, y = round(minv, -3)),
            label = glue("Minimum {minv} MW \n@ {min_day}"),
            hjust = 0,
            vjust = 1.5)

Figure 2.2: EGAT electricity sale profile to PEA-R1 in 2019.

outputfigure <- paste0(outfigdir, "pea_r1_central_egtsle_2019.png")
ggsave(profile_plot, file = outputfigure, dpi = 150, width = 15, height = 5, units = "in", limitsize = FALSE)
profiledata <- c(profiledata, list("pea_r1_central_egtsle_2019" = profile))
profilefigure <- c(profilefigure, list("pea_r1_central_egtsle_2019" = profile_plot))
summarydata <- c(summarydata, list("sum_pea_r1_central_egtsle_2019" = summary))

2.2.3 The 2019 PEA-R2 (North Eastern region) EGAT sale profile

# Profile data ####
profile <- 
  read_excel("raw_data/raw_data_profiles/02_Hourly Sale_NetGen_2019.xlsx",
             sheet = "Load Curve",
             range = "C3:F17523"
  ) %>% 
  select(datetime = `Date/Time`, PEA_R2) %>% 
  mutate(date = date(datetime),
         time = format(as.POSIXct(datetime),"%H:%M:%S"),
         year = year(datetime),
         month = month(datetime),
         day = day(datetime)) %>% 
  select(datetime, date, time, year, month, day, PEA_R2) #%>% 
# mutate(strdatetime = as.factor(strptime(glue("{year}-{month}-{day} {time}"), "%Y-%m-%d %H:%M:%S")))

Table 2.5: The 2019 EGAT sale to PEA R2 profile

datetime	date	time	year	month	day	PEA_R2
2019-01-01 00:00:00	2019-01-01	00:00:00	2019	1	1	1459.520
2019-01-01 00:30:00	2019-01-01	00:30:00	2019	1	1	1415.846
2019-01-01 01:00:00	2019-01-01	01:00:00	2019	1	1	1350.192
2019-01-01 01:30:00	2019-01-01	01:30:00	2019	1	1	1287.925
2019-01-01 02:00:00	2019-01-01	02:00:00	2019	1	1	1231.863

# Summary data ####
maxv <- ceiling(max(profile$PEA_R2)) # Get a peak MW
minv <- floor(min(profile$PEA_R2)) # Get a min MW
energy <- sum(profile$PEA_R2)/2000 # Calculate the energy
peak_day <- profile %>% #Find a peak day
  group_by(year) %>% 
  filter(PEA_R2 == max(PEA_R2)) %>% 
  pull(datetime)
min_day <- profile %>% #Find a peak day
  group_by(year) %>% 
  filter(PEA_R2 == min(PEA_R2)) %>% 
  pull(datetime)
load_factor <- percent((energy*10^3)/(maxv*8760), 
                       accuracy = 0.01, 
                       decimal.mark = ".")
summary <- tibble(peak_day = peak_day,
                  min_day = min_day,
                  peak_mw = maxv, 
                  min_mw = minv, 
                  energy_gwh = energy,
                  load_factor = load_factor) # combine all data in 1 table

Table 2.6: Summary of EGAT sale to PEA R1 in 2019

peak_day	min_day	peak_mw	min_mw	energy_gwh	load_factor
2019-04-20 21:00:00	2019-01-01 12:00:00	3803	970	20007.86	60.06%

# Plot a profile ####

profile_plot <-
  ggplot() + 
  geom_line(data=profile, 
            aes(x = datetime, 
                y = PEA_R2,
                group = month,
                color = as.factor(month)),
            show.legend = FALSE) +
  ThemeLine +
  labs(x = NULL,
       y = "EGAT sale to PEA R2 (North Eastern region) (MW)")+
  scale_x_datetime(breaks=date_breaks("1 month"), 
                   labels=date_format("%b %y")) +
  scale_y_continuous(breaks = seq(0, round(maxv,-3),1000),
                     limits = c(0, round(maxv, -3))) +  
  scale_color_manual(values = linepalette1) +
  geom_point(data=summary,
             aes(x = peak_day, y = peak_mw))+
  geom_text(data = summary,
            aes(x = peak_day, y = round(maxv, -3)),
            label = glue("Peak {maxv} MW \n@ {peak_day}"))+
  geom_point(data=summary,
             aes(x = min_day, y = min_mw))+
  geom_text(data = summary,
            aes(x = min_day, y = round(minv, -3)),
            label = glue("Minimum {minv} MW \n@ {min_day}"),
            hjust = 0,
            vjust = 1.5)

# Save the output ####
outputfigure <- paste0(outfigdir, "pea_r2_northeastern_egtsle_2019.png")
ggsave(profile_plot, file = outputfigure, dpi = 150, width = 15, height = 5, units = "in", limitsize = FALSE)
profiledata <- c(profiledata, list("pea_r2_northeastern_egtsle_2019" = profile))
profilefigure <- c(profilefigure, list("pea_r2_northeastern_egtsle_2019" = profile_plot))
summarydata <- c(summarydata, list("sum_pea_r2_northeastern_egtsle_2019" = summary))

The 2019 electricity sale profile from EGAT to PEA-R2 is illustrated in Figure 2.3).

Figure 2.3: EGAT electricity sale profile to PEA-R2 in 2019.